6 papers · 1 filter
Self-Exploring Language Models for Explainable Link Forecasting on Temporal Graphs via Reinforcement Learning
Zifeng Ding, Shenyang Huang, Zeyu Cao +11
Forecasting future links is a central task in temporal graph (TG) reasoning, requiring models to leverage historical interactions to predict upcoming ones. Traditional neural appro…
Loong: Synthesize Long Chain-of-Thoughts at Scale through Verifiers
Xingyue Huang, Rishabh, Gregor Franke +43
Recent advances in Large Language Models (LLMs) have shown that their reasoning capabilities can be significantly improved through Reinforcement Learning with Verifiable Reward (RL…
ArgRAG: Explainable Retrieval Augmented Generation using Quantitative Bipolar Argumentation
Yuqicheng Zhu, Nico Potyka, Daniel Hernández +6
Retrieval-Augmented Generation (RAG) enhances large language models by incorporating external knowledge, yet suffers from critical limitations in high-stakes domains -- namely, sen…
Distilling Tool Knowledge into Language Models via Back-Translated Traces
Xingyue Huang, Xianglong Hu, Zifeng Ding +9
Large language models (LLMs) often struggle with mathematical problems that require exact computation or multi-step algebraic reasoning. Tool-integrated reasoning (TIR) offers a pr…
Predicate-Conditional Conformalized Answer Sets for Knowledge Graph Embeddings
Yuqicheng Zhu, Daniel Hernández, Yuan He +4
Uncertainty quantification in Knowledge Graph Embedding (KGE) methods is crucial for ensuring the reliability of downstream applications. A recent work applies conformal prediction…
Supposedly Equivalent Facts That Aren't? Entity Frequency in Pre-training Induces Asymmetry in LLMs
Yuan He, Bailan He, Zifeng Ding +8
Understanding and mitigating hallucinations in Large Language Models (LLMs) is crucial for ensuring reliable content generation. While previous research has primarily focused on "w…